An online non-intrusive load monitoring method based on Hidden Markov model

被引：4
|
作者：
Huang, Xianqing [1 ]
Yin, Bo [1 ]
Wei, Zhiqiang [1 ]
Wei, Xinghao [1 ]
Zhang, Rui [2 ]
机构：
[1] Ocean Univ China, Coll Informat Sci & Engn, Qingdao, Shandong, Peoples R China
[2] Qingdao Haier Smart Technol R&D Co Ltd, Qingdao, Shandong, Peoples R China
来源：
2018 INTERNATIONAL SEMINAR ON COMPUTER SCIENCE AND ENGINEERING TECHNOLOGY (SCSET 2018) | 2019年 / 1176卷
关键词：
D O I：
10.1088/1742-6596/1176/4/042036
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Non-intrusive load monitoring (NILM) can decompose the total power consumption measured by the smart meter into the power consumed by the individual appliances, so as to achieve the purpose of saving energy. In this paper, an improved method of Daubechies9 (DB9) which is a discrete wavelet is proposed, which can effectively remove the noise of the low-frequency components. On this basis, an online NILM method based on Hidden Markov model (HMM) is proposed. The model of load switching can be built using apparent power of transient-state with this method. Besides, the improved forward algorithm which effectively suppressing the data underflow in load classification is proposed. The proposed methods are embedded in the smart meter and can increase the overall recognition rate of the load over 90% in the experiments which prove that they have good applicability.
引用
收藏
页数：7
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